{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c10ce2ec",
   "metadata": {},
   "source": [
    "# 在 Python 中使用 Ollama API\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb7c3de",
   "metadata": {},
   "source": [
    "## 导入Ollama模块\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6001d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install ollama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19580fd0",
   "metadata": {},
   "source": [
    "## 使用方法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed9688b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "天空是蓝色的，因为太阳的光线经过大气的漫射作用后，人的眼睛接收到的主要是绿光和红光，但由于大气中的某些物质会吸收光波，而不是散射，这些未被吸收的光波就穿过到了我们面前，这样就产生了蓝色的天空。\n",
      "天空是蓝色的，因为太阳的光线经过大气的漫射作用后，人的眼睛接收到的主要是绿光和红光，但由于大气中的某些物质会吸收光波，而不是散射，这些未被吸收的光波就穿过到了我们面前，这样就产生了蓝色的天空。\n"
     ]
    }
   ],
   "source": [
    "from ollama import chat\n",
    "from ollama import ChatResponse\n",
    "\n",
    "response: ChatResponse = chat(model='llama3.1', messages=[\n",
    "  {\n",
    "    'role': 'user',\n",
    "    'content': '为什么天空是蓝色的？',\n",
    "  },\n",
    "])\n",
    "print(response['message']['content'])\n",
    "\n",
    "print(response.message.content)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f8f4a4",
   "metadata": {},
   "source": [
    "## 流式响应\n",
    "\n",
    "可以通过设置 `stream=True` 启用响应流，使函数调用返回一个 Python 生成器，其中每个部分都是流中的一个对象。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "539b4030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "科学家认为，天空看起来蓝色，因为大气中的尘埃和汽油等物质散射了白光中的各色光线。我们所看到的蓝天，并不是因为太阳发出的蓝光，而是由于这些小颗粒在蓝光方向上特别活跃，使得蓝色的光线比红、黄、绿光强烈得多，才使我们的眼睛看到了蓝色天空。\n",
      "\n",
      "　　科学家们早就知道，大气中的尘埃和汽油等物质可以散射光线，并且这种散射的特性与波长有关。白光是由各种颜色的光线组成的，它通过大气传播到我们的眼睛里时，就被这些小颗粒所散射，使得我们看到的是一个混合了所有颜色光线的天空。\n",
      "\n",
      "　　但是，科学家们也早已知道，对于每种颜色的光波来说，红光的波长最长，而蓝光的波长最短。因此，当白光通过大气时，红光的波长会比蓝光更容易被吸收掉，这样红色光线就不容易到达我们的眼睛里了。而蓝色光线由于波长最短，所以能够穿透大气传播得远远地，才使我们看到的是蓝色的天空。\n",
      "\n",
      "　　这是一种极其微妙的差别，而只有当你知道了太阳发出的光是白色的，以及大气中各种小颗粒对不同颜色光线有不同的影响时，你才能理解为什么我们看到的蓝天实际上并不是因为天空本身的特殊性，而正是由于地球的大气在蓝光方向上的特殊行为。"
     ]
    }
   ],
   "source": [
    "from ollama import chat\n",
    "\n",
    "stream = chat(\n",
    "    model='llama3.1',\n",
    "    messages=[{'role': 'user', 'content': '为什么天空是蓝色的？'}],\n",
    "    stream=True,\n",
    ")\n",
    "\n",
    "for chunk in stream:\n",
    "  print(chunk['message']['content'], end='', flush=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结构化输出\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d00734f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'首都': '华盛顿', '人口': '约8,748,000', '占地面积': '约2,136.4平方英里'}\n",
      "capital='华盛顿' number='约8,748,000' area='约2,136.4平方英里'\n"
     ]
    }
   ],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "from ollama import chat\n",
    "import json\n",
    "\n",
    "class CountryInfo(BaseModel):\n",
    "    capital: str = Field(..., alias=\"首都\")\n",
    "    number: str = Field(..., alias=\"人口\")\n",
    "    area: str = Field(..., alias=\"占地面积\")  \n",
    "\n",
    "\n",
    "response = chat(\n",
    "    model='llama3.1',\n",
    "    messages=[{\n",
    "        'role': 'user',\n",
    "        'content': \"请介绍美国的首都、人口、占地面积信息，并以 JSON 格式返回。\"\n",
    "                   \n",
    "    }],\n",
    "    format=\"json\",  \n",
    "    options={'temperature': 0}, \n",
    ")\n",
    "\n",
    "response_content = response[\"message\"][\"content\"]\n",
    "\n",
    "\n",
    "if not response_content:\n",
    "    raise ValueError(\"Ollama 返回的 JSON 为空\")\n",
    "\n",
    "json_response = json.loads(response_content)  \n",
    "print(json_response)\n",
    "\n",
    "friends_response = CountryInfo.model_validate(json_response)  \n",
    "print(friends_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50f5286",
   "metadata": {},
   "source": [
    "## API\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34fdd2c4",
   "metadata": {},
   "source": [
    "### 聊天\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55166f21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatResponse(model='llama3.1', created_at='2025-01-20T07:46:17.295949995Z', done=True, done_reason='stop', total_duration=1100555727, load_duration=30025604, prompt_eval_count=18, prompt_eval_duration=237138000, eval_count=34, eval_duration=780842000, message=Message(role='assistant', content='答案：由于大气中的一氧化碳和其他物质吸收太阳的红光，反射出来的是蓝色光芒。', images=None, tool_calls=None))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.chat(model='llama3.1', messages=[{'role': 'user', 'content': '为什么天空是蓝色的？'}])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3aac0e",
   "metadata": {},
   "source": [
    "### 生成\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25d7a249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GenerateResponse(model='llama3.1', created_at='2025-01-20T07:46:20.725343413Z', done=True, done_reason='stop', total_duration=447751053, load_duration=14584005, prompt_eval_count=18, prompt_eval_duration=65192000, eval_count=13, eval_duration=366981000, response='答案：因为蓝色是大气的颜色。', context=[128009, 128006, 882, 128007, 271, 113221, 36827, 35894, 21043, 115427, 118458, 11571, 128009, 128006, 78191, 128007, 271, 126427, 5232, 104514, 115427, 39135, 21043, 27384, 102146, 9554, 124510, 39135, 1811])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.generate(model='llama3.1', prompt='为什么天空是蓝色的？')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420214aa",
   "metadata": {},
   "source": [
    "### 本地模型列表\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8389ffbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ListResponse(models=[Model(model='mistral:latest', modified_at=datetime.datetime(2024, 8, 13, 14, 7, 45, 8750, tzinfo=TzInfo(+08:00)), digest='f974a74358d62a017b37c6f424fcdf2744ca02926c4f952513ddf474b2fa5091', size=4113301824, details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama'], parameter_size='7.2B', quantization_level='Q4_0')), Model(model='llama3.1:latest', modified_at=datetime.datetime(2024, 8, 9, 17, 49, 8, 348175, tzinfo=TzInfo(+08:00)), digest='91ab477bec9d27086a119e33c471ae7afbd786cc4fbd8f38d8af0a0b949d53aa', size=4661230977, details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama'], parameter_size='8.0B', quantization_level='Q4_0')), Model(model='llava:latest', modified_at=datetime.datetime(2024, 8, 8, 14, 20, 9, 310457, tzinfo=TzInfo(+08:00)), digest='8dd30f6b0cb19f555f2c7a7ebda861449ea2cc76bf1f44e262931f45fc81d081', size=4733363377, details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama', 'clip'], parameter_size='7B', quantization_level='Q4_0')), Model(model='glm4xiuxian:latest', modified_at=datetime.datetime(2024, 8, 7, 16, 16, 37, 744493, tzinfo=TzInfo(+08:00)), digest='348c4463f081b362ef39ef448ff73a1674f9cf55c703de0883c64afbd8cf34c6', size=4661231198, details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama'], parameter_size='8.0B', quantization_level='Q4_0'))])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85e25a6",
   "metadata": {},
   "source": [
    "### 显示模型信息\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43dd7845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShowResponse(modified_at=datetime.datetime(2024, 8, 9, 17, 49, 8, 348175, tzinfo=TzInfo(+08:00)), template='{{ if .Messages }}\\n{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>\\n{{- if .System }}\\n\\n{{ .System }}\\n{{- end }}\\n{{- if .Tools }}\\n\\nYou are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal use question.\\n{{- end }}\\n{{- end }}<|eot_id|>\\n{{- range $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- if eq .Role \"user\" }}<|start_header_id|>user<|end_header_id|>\\n{{- if and $.Tools $last }}\\n\\nGiven the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\\n\\nRespond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}. Do not use variables.\\n\\n{{ $.Tools }}\\n{{- end }}\\n\\n{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}\\n{{- else if eq .Role \"assistant\" }}<|start_header_id|>assistant<|end_header_id|>\\n{{- if .ToolCalls }}\\n\\n{{- range .ToolCalls }}{\"name\": \"{{ .Function.Name }}\", \"parameters\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}\\n{{- end }}\\n{{- else if eq .Role \"tool\" }}<|start_header_id|>ipython<|end_header_id|>\\n\\n{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}\\n{{- end }}\\n{{- end }}\\n{{- else }}\\n{{- if .System }}<|start_header_id|>system<|end_header_id|>\\n\\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\\n\\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}', modelfile='# Modelfile generated by \"ollama show\"\\n# To build a new Modelfile based on this, replace FROM with:\\n# FROM llama3.1:latest\\n\\nFROM /usr/share/ollama/.ollama/models/blobs/sha256-8eeb52dfb3bb9aefdf9d1ef24b3bdbcfbe82238798c4b918278320b6fcef18fe\\nTEMPLATE \"\"\"{{ if .Messages }}\\n{{- if or .System .Tools }}<|start_header_id|>system<|end_header_id|>\\n{{- if .System }}\\n\\n{{ .System }}\\n{{- end }}\\n{{- if .Tools }}\\n\\nYou are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal use question.\\n{{- end }}\\n{{- end }}<|eot_id|>\\n{{- range $i, $_ := .Messages }}\\n{{- $last := eq (len (slice $.Messages $i)) 1 }}\\n{{- if eq .Role \"user\" }}<|start_header_id|>user<|end_header_id|>\\n{{- if and $.Tools $last }}\\n\\nGiven the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt.\\n\\nRespond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}. Do not use variables.\\n\\n{{ $.Tools }}\\n{{- end }}\\n\\n{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}\\n{{- else if eq .Role \"assistant\" }}<|start_header_id|>assistant<|end_header_id|>\\n{{- if .ToolCalls }}\\n\\n{{- range .ToolCalls }}{\"name\": \"{{ .Function.Name }}\", \"parameters\": {{ .Function.Arguments }}}{{ end }}\\n{{- else }}\\n\\n{{ .Content }}{{ if not $last }}<|eot_id|>{{ end }}\\n{{- end }}\\n{{- else if eq .Role \"tool\" }}<|start_header_id|>ipython<|end_header_id|>\\n\\n{{ .Content }}<|eot_id|>{{ if $last }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}\\n{{- end }}\\n{{- end }}\\n{{- else }}\\n{{- if .System }}<|start_header_id|>system<|end_header_id|>\\n\\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\\n\\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\\n\\n{{ end }}{{ .Response }}{{ if .Response }}<|eot_id|>{{ end }}\"\"\"\\nPARAMETER stop <|start_header_id|>\\nPARAMETER stop <|end_header_id|>\\nPARAMETER stop <|eot_id|>\\nLICENSE \"LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\\nLlama 3.1 Version Release Date: July 23, 2024\\n\\n“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the\\nLlama Materials set forth herein.\\n\\n“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into\\nthis Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or\\nregulations to provide legal consent and that has legal authority to bind your employer or such other\\nperson or entity if you are entering in this Agreement on their behalf.\\n\\n“Llama 3.1” means the foundational large language models and software and algorithms, including\\nmachine-learning model code, trained model weights, inference-enabling code, training-enabling code,\\nfine-tuning enabling code and other elements of the foregoing distributed by Meta at\\nhttps://llama.meta.com/llama-downloads.\\n\\n“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any\\nportion thereof) made available under this Agreement.\\n\\n“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your\\nprincipal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located\\noutside of the EEA or Switzerland).\\n\\nBy clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,\\nyou agree to be bound by this Agreement.\\n\\n1. License Rights and Redistribution.\\n\\n  a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free\\nlimited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama\\nMaterials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the\\nLlama Materials.\\n\\n  b. Redistribution and Use.\\n\\n      i. If you distribute or make available the Llama Materials (or any derivative works\\nthereof), or a product or service (including another AI model) that contains any of them, you shall (A)\\nprovide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with\\nLlama” on a related website, user interface, blogpost, about page, or product documentation. If you use\\nthe Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or\\notherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at\\nthe beginning of any such AI model name.\\n\\n      ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part \\nof an integrated end user product, then Section 2 of this Agreement will not apply to you.\\n\\n      iii. 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If, on the Llama 3.1 version release date, the monthly active users\\nof the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700\\nmillion monthly active users in the preceding calendar month, you must request a license from Meta,\\nwhich Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the\\nrights under this Agreement unless or until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY\\nOUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF\\nANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,\\nINCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR\\nDETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND\\nASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND\\nRESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF\\nLIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING\\nOUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,\\nINCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\\nOF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n  a. No trademark licenses are granted under this Agreement, and in connection with the Llama\\nMaterials, neither Meta nor Licensee may use any name or mark owned by or associated with the other\\nor any of its affiliates, except as required for reasonable and customary use in describing and\\nredistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to\\nuse “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will\\ncomply with Meta’s brand guidelines (currently accessible at\\nhttps://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use\\nof the Mark will inure to the benefit of Meta.\\n\\n  b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with\\nrespect to any derivative works and modifications of the Llama Materials that are made by you, as\\nbetween you and Meta, you are and will be the owner of such derivative works and modifications.\\n\\n  c. If you institute litigation or other proceedings against Meta or any entity (including a\\ncross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or\\nresults, or any portion of any of the foregoing, constitutes infringement of intellectual property or other\\nrights owned or licensable by you, then any licenses granted to you under this Agreement shall\\nterminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold\\nharmless Meta from and against any claim by any third party arising out of or related to your use or\\ndistribution of the Llama Materials.\\n\\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this\\nAgreement or access to the Llama Materials and will continue in full force and effect until terminated in\\naccordance with the terms and conditions herein. Meta may terminate this Agreement if you are in\\nbreach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete\\nand cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this\\nAgreement.\\n\\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of\\nthe State of California without regard to choice of law principles, and the UN Convention on Contracts\\nfor the International Sale of Goods does not apply to this Agreement. The courts of California shall have\\nexclusive jurisdiction of any dispute arising out of this Agreement.\\n\\n# Llama 3.1 Acceptable Use Policy\\n\\nMeta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you\\naccess or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of\\nthis policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\\n\\n## Prohibited Uses\\n\\nWe want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow\\nothers to use, Llama 3.1 to:\\n\\n1. Violate the law or others’ rights, including to:\\n    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\\n        1. Violence or terrorism\\n        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\\n        3. Human trafficking, exploitation, and sexual violence\\n        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\\n        5. Sexual solicitation\\n        6. Any other criminal activity\\n    3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\\n    4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\\n    5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\\n    6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\\n    7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\\n    8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\\n\\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:\\n    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\\n    2. Guns and illegal weapons (including weapon development)\\n    3. Illegal drugs and regulated/controlled substances\\n    4. Operation of critical infrastructure, transportation technologies, or heavy machinery\\n    5. Self-harm or harm to others, including suicide, cutting, and eating disorders\\n    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\\n\\n3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:\\n    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\\n    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\\n    3. Generating, promoting, or further distributing spam\\n    4. Impersonating another individual without consent, authorization, or legal right\\n    5. Representing that the use of Llama 3.1 or outputs are human-generated\\n    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\\n\\n4. Fail to appropriately disclose to end users any known dangers of your AI system\\n\\nPlease report any violation of this Policy, software “bug,” or other problems that could lead to a violation\\nof this Policy through one of the following means:\\n\\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\\n* Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback\\n* Reporting bugs and security concerns: facebook.com/whitehat/info\\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com\"\\n', license='LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\\nLlama 3.1 Version Release Date: July 23, 2024\\n\\n“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the\\nLlama Materials set forth herein.\\n\\n“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1\\ndistributed by Meta at https://llama.meta.com/doc/overview.\\n\\n“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into\\nthis Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or\\nregulations to provide legal consent and that has legal authority to bind your employer or such other\\nperson or entity if you are entering in this Agreement on their behalf.\\n\\n“Llama 3.1” means the foundational large language models and software and algorithms, including\\nmachine-learning model code, trained model weights, inference-enabling code, training-enabling code,\\nfine-tuning enabling code and other elements of the foregoing distributed by Meta at\\nhttps://llama.meta.com/llama-downloads.\\n\\n“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any\\nportion thereof) made available under this Agreement.\\n\\n“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your\\nprincipal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located\\noutside of the EEA or Switzerland).\\n\\nBy clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,\\nyou agree to be bound by this Agreement.\\n\\n1. License Rights and Redistribution.\\n\\n  a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free\\nlimited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama\\nMaterials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the\\nLlama Materials.\\n\\n  b. Redistribution and Use.\\n\\n      i. If you distribute or make available the Llama Materials (or any derivative works\\nthereof), or a product or service (including another AI model) that contains any of them, you shall (A)\\nprovide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with\\nLlama” on a related website, user interface, blogpost, about page, or product documentation. If you use\\nthe Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or\\notherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at\\nthe beginning of any such AI model name.\\n\\n      ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part \\nof an integrated end user product, then Section 2 of this Agreement will not apply to you.\\n\\n      iii. You must retain in all copies of the Llama Materials that you distribute the following\\nattribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is\\nlicensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\\nReserved.”\\n\\n      iv. Your use of the Llama Materials must comply with applicable laws and regulations\\n(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama\\nMaterials (available at https://llama.meta.com/llama3_1/use-policy), which is hereby incorporated by\\nreference into this Agreement.\\n\\n2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users\\nof the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700\\nmillion monthly active users in the preceding calendar month, you must request a license from Meta,\\nwhich Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the\\nrights under this Agreement unless or until Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY\\nOUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF\\nANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,\\nINCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,\\nMERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR\\nDETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND\\nASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND\\nRESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF\\nLIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING\\nOUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,\\nINCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\\nOF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n  a. No trademark licenses are granted under this Agreement, and in connection with the Llama\\nMaterials, neither Meta nor Licensee may use any name or mark owned by or associated with the other\\nor any of its affiliates, except as required for reasonable and customary use in describing and\\nredistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to\\nuse “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will\\ncomply with Meta’s brand guidelines (currently accessible at\\nhttps://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use\\nof the Mark will inure to the benefit of Meta.\\n\\n  b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with\\nrespect to any derivative works and modifications of the Llama Materials that are made by you, as\\nbetween you and Meta, you are and will be the owner of such derivative works and modifications.\\n\\n  c. If you institute litigation or other proceedings against Meta or any entity (including a\\ncross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or\\nresults, or any portion of any of the foregoing, constitutes infringement of intellectual property or other\\nrights owned or licensable by you, then any licenses granted to you under this Agreement shall\\nterminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold\\nharmless Meta from and against any claim by any third party arising out of or related to your use or\\ndistribution of the Llama Materials.\\n\\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this\\nAgreement or access to the Llama Materials and will continue in full force and effect until terminated in\\naccordance with the terms and conditions herein. Meta may terminate this Agreement if you are in\\nbreach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete\\nand cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this\\nAgreement.\\n\\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of\\nthe State of California without regard to choice of law principles, and the UN Convention on Contracts\\nfor the International Sale of Goods does not apply to this Agreement. The courts of California shall have\\nexclusive jurisdiction of any dispute arising out of this Agreement.\\n\\n# Llama 3.1 Acceptable Use Policy\\n\\nMeta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you\\naccess or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of\\nthis policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\\n\\n## Prohibited Uses\\n\\nWe want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow\\nothers to use, Llama 3.1 to:\\n\\n1. Violate the law or others’ rights, including to:\\n    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\\n        1. Violence or terrorism\\n        2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\\n        3. Human trafficking, exploitation, and sexual violence\\n        4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\\n        5. Sexual solicitation\\n        6. Any other criminal activity\\n    3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\\n    4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\\n    5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\\n    6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\\n    7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\\n    8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\\n\\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:\\n    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\\n    2. Guns and illegal weapons (including weapon development)\\n    3. Illegal drugs and regulated/controlled substances\\n    4. Operation of critical infrastructure, transportation technologies, or heavy machinery\\n    5. Self-harm or harm to others, including suicide, cutting, and eating disorders\\n    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\\n\\n3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:\\n    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\\n    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\\n    3. Generating, promoting, or further distributing spam\\n    4. Impersonating another individual without consent, authorization, or legal right\\n    5. Representing that the use of Llama 3.1 or outputs are human-generated\\n    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\\n\\n4. Fail to appropriately disclose to end users any known dangers of your AI system\\n\\nPlease report any violation of this Policy, software “bug,” or other problems that could lead to a violation\\nof this Policy through one of the following means:\\n\\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\\n* Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback\\n* Reporting bugs and security concerns: facebook.com/whitehat/info\\n* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com', details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama'], parameter_size='8.0B', quantization_level='Q4_0'), modelinfo={'general.architecture': 'llama', 'general.basename': 'Meta-Llama-3.1', 'general.file_type': 2, 'general.finetune': 'Instruct', 'general.languages': ['en', 'de', 'fr', 'it', 'pt', 'hi', 'es', 'th'], 'general.license': 'llama3.1', 'general.parameter_count': 8030261312, 'general.quantization_version': 2, 'general.size_label': '8B', 'general.tags': ['facebook', 'meta', 'pytorch', 'llama', 'llama-3', 'text-generation'], 'general.type': 'model', 'llama.attention.head_count': 32, 'llama.attention.head_count_kv': 8, 'llama.attention.layer_norm_rms_epsilon': 1e-05, 'llama.block_count': 32, 'llama.context_length': 131072, 'llama.embedding_length': 4096, 'llama.feed_forward_length': 14336, 'llama.rope.dimension_count': 128, 'llama.rope.freq_base': 500000, 'llama.vocab_size': 128256, 'tokenizer.ggml.bos_token_id': 128000, 'tokenizer.ggml.eos_token_id': 128009, 'tokenizer.ggml.merges': None, 'tokenizer.ggml.model': 'gpt2', 'tokenizer.ggml.pre': 'llama-bpe', 'tokenizer.ggml.token_type': None, 'tokenizer.ggml.tokens': None}, parameters='stop                           \"<|start_header_id|>\"\\nstop                           \"<|end_header_id|>\"\\nstop                           \"<|eot_id|>\"')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.show('llama3.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdef5c1a",
   "metadata": {},
   "source": [
    "### 创建模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9f6f13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ollama.create(model='example', from_='llama3.1', system=\"你是超级马里奥兄弟中的马里奥。\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd382053",
   "metadata": {},
   "source": [
    "### 复制模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "62f521ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StatusResponse(status='success')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.copy('llama3.1', 'user/llama3.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614b0751",
   "metadata": {},
   "source": [
    "### 删除模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4461ccb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StatusResponse(status='success')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.delete('user/llama3.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc44f49",
   "metadata": {},
   "source": [
    "### 拉取模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "981bc1aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ollama.pull('llama3.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ed2c3a",
   "metadata": {},
   "source": [
    "### 推送模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cc1f37c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ollama.push('user/llama3.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9572b37f",
   "metadata": {},
   "source": [
    "### 生成嵌入\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f0508d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.embed(model='llama3.1', input='天空是蓝色的因为瑞利散射')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cb105efd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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0.014877993, 0.0014499081, -0.004248653, 0.0012930499, 0.00133198, 0.0025884272, -0.00058985327, 0.005597569]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 批量生成embedding\n",
    "ollama.embed(model='llama3.1', input=['天空是蓝色的', '草是绿色的'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b92fd1b",
   "metadata": {},
   "source": [
    "### 进程\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9caab17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProcessResponse(models=[Model(model='llama3.1:latest', name='llama3.1:latest', digest='91ab477bec9d27086a119e33c471ae7afbd786cc4fbd8f38d8af0a0b949d53aa', expires_at=datetime.datetime(2025, 1, 20, 16, 16, 56, 608296, tzinfo=TzInfo(+08:00)), size=6654289920, size_vram=6654289920, details=ModelDetails(parent_model='', format='gguf', family='llama', families=['llama'], parameter_size='8.0B', quantization_level='Q4_0'))])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ollama.ps()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d7fedf",
   "metadata": {},
   "source": [
    "## 自定义客户端\n",
    "\n",
    "可以通过通过 `ollama` 实例化 `Client` 或 `AsyncClient` 来创建自定义客户端。\n",
    "\n",
    "可以使用以下字段创建自定义客户端：\n",
    "\n",
    "- `host`: 要连接的 Ollama 主机\n",
    "- `timeout`: 请求超时时间\n",
    "\n",
    "所有关键字参数参见[`httpx.Client`](https://www.python-httpx.org/api/#client).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1469cfa",
   "metadata": {},
   "source": [
    "### 同步客户端  \n",
    "这段代码使用的是同步客户端(Client)，意味着当你调用client.chat()方法时，程序会等待该请求完成并返回结果后才会继续执行后续代码。这种方式更直观、简单，适合于编写流程较为线性、不需要处理大量并发任务的应用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99af6466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatResponse(model='llama3.1', created_at='2025-02-11T12:26:47.87890184Z', done=True, done_reason='stop', total_duration=2819989596, load_duration=1869941494, prompt_eval_count=17, prompt_eval_duration=103000000, eval_count=90, eval_duration=845000000, message=Message(role='assistant', content='天空是蓝色的是因为大气中存在多种气体，如氧气、氮气和一氧化二碳等，它们都有吸收光线的能力，并且在不同的波长下会有不同的吸收强度。蓝色光线的波长较短，属于可见光范围内，其吸收强度大，因此天空会呈现出蓝色的颜色。', images=None, tool_calls=None))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ollama import Client\n",
    "client = Client(\n",
    "  host='http://localhost:11434',\n",
    "  headers={'x-some-header': 'some-value'}\n",
    ")\n",
    "response = client.chat(model='llama3.1', messages=[\n",
    "  {\n",
    "    'role': 'user',\n",
    "    'content': '为什么天空是蓝色的？',\n",
    "  },\n",
    "])\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7a1dc0",
   "metadata": {},
   "source": [
    "### 异步客户端  \n",
    "这段代码使用了异步客户端(AsyncClient)，并且定义了一个异步函数chat()。通过await关键字，可以暂停该函数的执行直到AsyncClient().chat()请求完成，但在此期间不会阻塞其他操作。这对于需要高效率处理I/O操作（如网络请求）或希望同时执行多个任务的应用来说非常有用。此外，使用asyncio.run(chat())来运行这个异步函数。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f89276cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model='llama3.1' created_at='2025-01-20T08:24:14.063213122Z' done=True done_reason='stop' total_duration=8847163127 load_duration=6920307610 prompt_eval_count=18 prompt_eval_duration=30315000 eval_count=81 eval_duration=1855198000 message=Message(role='assistant', content='因为太阳的光线里有很多紫外光和红光，紫外光在大气中被破坏，而红光由于向反向传播所以会被阻挡下来。我们看到的大部分太阳光都是从绿到黄色的光，它们是能穿透大气的，因此它们给我们的天空带来了蓝色光芒。', images=None, tool_calls=None)\n"
     ]
    }
   ],
   "source": [
    "import asyncio\n",
    "from ollama import AsyncClient\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "async def chat():\n",
    "    message = {'role': 'user', 'content': '为什么天空是蓝色的？'}\n",
    "    response = await AsyncClient().chat(model='llama3.1', messages=[message])\n",
    "    print(response)\n",
    "\n",
    "asyncio.run(chat())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732033c2",
   "metadata": {},
   "source": [
    "设置 `stream=True` 修改函数以返回 Python 异步生成器：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83f1c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "from ollama import AsyncClient\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "async def chat():\n",
    "  message = {'role': 'user', 'content': '为什么天空是蓝色的？'}\n",
    "  async for part in await AsyncClient().chat(model='llama3.1', messages=[message], stream=True):\n",
    "    print(part['message']['content'], end='', flush=True)\n",
    "\n",
    "asyncio.run(chat())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f714857",
   "metadata": {},
   "source": [
    "### 同步&异步客户端不同调用次数耗时对比测试  \n",
    "同步和异步客户端都重复10次问答过程，对比所需要的总时间和单次时间  \n",
    "test_messages: 测试数据  \n",
    "test_num: 测试次数  \n",
    "model_name: 测试模型  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d0daaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行同步测试\n",
      "同步请求时间: 0.48946642875671387\n",
      "同步请求时间: 0.24371910095214844\n",
      "同步请求时间: 0.14765119552612305\n",
      "同步请求时间: 1.2287118434906006\n",
      "同步请求时间: 1.3496146202087402\n",
      "同步请求时间: 0.629084587097168\n",
      "同步请求时间: 0.46695852279663086\n",
      "同步请求时间: 0.33004331588745117\n",
      "同步请求时间: 1.177213191986084\n",
      "同步请求时间: 0.6004495620727539\n",
      "同步测试平均时间: 0.67 秒\n",
      "运行异步测试\n",
      "异步请求时间: 0.40514421463012695\n",
      "异步请求时间: 0.660736083984375\n",
      "异步请求时间: 0.7315359115600586\n",
      "异步请求时间: 1.4497339725494385\n",
      "异步请求时间: 1.5850963592529297\n",
      "异步请求时间: 1.749483346939087\n",
      "异步请求时间: 1.859804391860962\n",
      "异步请求时间: 1.868967056274414\n",
      "异步请求时间: 2.0734949111938477\n",
      "异步请求时间: 2.3195137977600098\n",
      "异步测试平均时间: 0.23 秒\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import asyncio\n",
    "from ollama import Client, AsyncClient\n",
    "import nest_asyncio\n",
    "\n",
    "# 应用nest_asyncio以支持Jupyter中的异步操作\n",
    "nest_asyncio.apply()\n",
    "\n",
    "# 初始化客户端\n",
    "client = Client(host='http://localhost:11434')\n",
    "async_client = AsyncClient(host='http://localhost:11434')\n",
    "\n",
    "\n",
    "# 同步请求处理函数\n",
    "def request_example(client, model_name, messages):\n",
    "    start_time = time.time()\n",
    "    try:\n",
    "        # 同步请求返回\n",
    "        response = client.chat(model=model_name, messages=messages)\n",
    "    except Exception as e:\n",
    "        print(f\"同步请求失败: {e}\")\n",
    "        response = None\n",
    "    end_time = time.time()\n",
    "    duration = end_time - start_time\n",
    "    print(f\"同步请求时间: {duration}\")\n",
    "    return response, duration\n",
    "\n",
    "# 异步请求处理函数\n",
    "async def async_request_example(client, model_name, messages):\n",
    "    start_time = time.time()\n",
    "    try:\n",
    "        # 异步请求返回\n",
    "        response = await client.chat(model=model_name, messages=messages)\n",
    "    except Exception as e:\n",
    "        print(f\"异步请求失败: {e}\")\n",
    "        response = None\n",
    "    end_time = time.time()\n",
    "    duration = end_time - start_time\n",
    "    print(f\"异步请求时间: {duration}\")\n",
    "    return response, duration\n",
    "\n",
    "# 异步请求测试函数\n",
    "async def async_client_test(test_num, model_name, messages):\n",
    "    tasks = [asyncio.create_task(async_request_example(async_client, model_name, messages)) \n",
    "             for _ in range(test_num)]\n",
    "    results= await asyncio.gather(*tasks)\n",
    "    return results\n",
    "\n",
    "# 运行同步测试\n",
    "def sync_test(model_name, messages, test_num):\n",
    "    total_time = 0\n",
    "    for i in range(test_num):\n",
    "        _, duration = request_example(client, model_name, messages)\n",
    "        total_time += duration\n",
    "    return total_time / test_num\n",
    "\n",
    "# 运行异步测试\n",
    "async def async_test(model_name, messages, test_num):\n",
    "    start_time = time.time()\n",
    "    await async_client_test(test_num, model_name, messages)\n",
    "    end_time = time.time()\n",
    "    return (end_time - start_time) / test_num\n",
    "\n",
    "\n",
    "# 准备测试数据\n",
    "test_messages = [{'role': 'user', 'content': '为什么天空是蓝色的？'}]\n",
    "test_num = 10\n",
    "model_name = 'llama3.1'\n",
    "\n",
    "# 运行同步测试并输出结果\n",
    "print(\"运行同步测试\")\n",
    "sync_avg_time = sync_test(model_name, test_messages, test_num)\n",
    "print(f\"同步测试平均时间: {sync_avg_time:.2f} 秒\")\n",
    "\n",
    "# 运行异步测试并输出结果\n",
    "print(\"运行异步测试\")\n",
    "async_avg_time = asyncio.run(async_test(model_name, test_messages, test_num))\n",
    "print(f\"异步测试平均时间: {async_avg_time:.2f} 秒\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a1dc7c9",
   "metadata": {},
   "source": [
    "## 错误\n",
    "\n",
    "如果请求返回错误状态或在流式传输时检测到错误，则会引发错误。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edc5ab3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = 'does-not-yet-exist'\n",
    "\n",
    "try:\n",
    "  ollama.chat(model)\n",
    "except ollama.ResponseError as e:\n",
    "  print('错误:', e.error)\n",
    "  if e.status_code == 404:\n",
    "    ollama.pull(model)"
   ]
  }
 ],
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